ruvnet--RuView/docs/benchmarks/person-count-cog.md

cog-person-count — Benchmark Log

Append-only log of every published count_v1 training run per ADR-103. New runs add a section; never overwrite history.

v0.0.1 — first measured run (2026-05-21)

Setup

Component	Value
Training host	ruvultra (Ubuntu, x86_64, RTX 5080)
Backend	PyTorch 2.12 + CUDA
Data	data/paired/wiflow-p7-1779210883.paired.jsonl — 1,077 paired samples, single 30-min session, label distribution {0: 533, 1: 544}
Train/eval split	80/20 stratified on ts_start (held-out tail of the recording)
Architecture	Conv1d encoder (56→64→128→128, dilations 1/2/4) + Linear(128→64→8) count head + Linear(128→32→1) confidence head — bit-identical to v2/crates/cog-person-count/src/inference.rs::CountNet
Loss	cross_entropy(count) + 0.3·BCE(conf) + 0.1·Brier(conf) with per-class weighting
Optimizer	AdamW, lr 1e-3, cosine warm restarts (T_0=50)
Z-score normalisation	per-subcarrier on train statistics, applied to eval
Epochs	400
Wall time	5.6 s

Accuracy (held-out 215-sample tail of the 30-min recording)

Metric	Value
Best eval accuracy	65.1%
Final eval accuracy	65.1%
Within ±1	100% (labels are all in {0, 1}, predictions trivially within ±1)
MAE	0.349 persons
Class 0 ("empty") accuracy	100% (140 samples)
Class 1 ("1 person") accuracy	0% (75 samples)
Confidence↔correctness Spearman	0.023

Honest read

The model overfit hard. By epoch 100 train_acc reached 1.0 and eval_loss climbed from 0.67 → 7.8. The "best" checkpoint (epoch ~2-3) is the snapshot that happened to predict mostly class-0 across eval, which matches the held-out window's class distribution (140/215 = 65.1%) — i.e. it learned the distribution of the tail of the recording, not a real empty-vs-occupied classifier.

Why: the training data is one continuous 30-minute solo recording. The held-out tail captures a stretch where the operator stepped away from the desk for stretches at a time, so the eval set is class-0-heavy and the model finds a degenerate "always predict 0" minimum that gets the eval distribution exactly right. Class 1 accuracy = 0 is the smoking gun.

Same data-bound failure mode as pose_v1 (#645). Same fix path: multi-room paired recordings.

What v0.0.1 still validates

• Pipeline correctness end-to-end. The Rust cog loaded the PyTorch-trained safetensors successfully on first try (backend: candle-cpu reported by cog-person-count health), confirming the architecture in src/inference.rs is byte-compatible with train-count.py.
• ONNX parity. 16 KB ONNX, exports cleanly under opset 18 with dynamic batch axis.
• Fast iteration loop. 5.6 s end-to-end training means we can sweep hyperparameters or retrain on new data in seconds, not hours.
• Cog binary size. Same 2.36 MB stripped release binary (no change — model loads at runtime via mmap'd safetensors).

Comparison to ADR-103 v0.1.0 targets

Gate	Target	Today	Status
Day-0 same-room accuracy within ±1	≥ 80%	100% (trivially — labels span {0,1})	met
Cross-room accuracy within ±1	≥ 60%	Not measured (no cross-room data)	deferred to v0.2.0
MAE	≤ 0.6	0.349	met
Per-frame confidence reflects accuracy (Spearman)	r ≥ 0.5	0.023	NOT MET
Inference latency on Pi 5	< 5 ms / frame	Not yet measured (cross-compile pending)	deferred
Binary size on GCS	≤ 4 MB	2.36 MB	met

The accuracy ones look "met" only because the labels collapse to {0, 1} and "within ±1" with 8 classes is trivially satisfied. The confidence calibration is the real failure for v0.0.1 — Spearman 0.023 means the confidence head is essentially random noise. That's also bounded by data scarcity; multi-session training should sharpen it.

Artifacts

• v2/crates/cog-person-count/cog/artifacts/count_v1.safetensors — 392 KB
• v2/crates/cog-person-count/cog/artifacts/count_v1.onnx — 16 KB
• v2/crates/cog-person-count/cog/artifacts/count_train_results.json — full per-epoch loss curve + hyperparameters + per-class breakdown

Reproducibility

# On any host with PyTorch + CUDA (cargo path not needed for training):
scp data/paired/wiflow-p7-1779210883.paired.jsonl <host>:/tmp/
scp scripts/train-count.py <host>:/tmp/
ssh <host> "cd /tmp && python3 train-count.py --paired wiflow-p7-1779210883.paired.jsonl --epochs 400"

Loads in the Rust cog with no translation step (safetensors layout matches cog-person-count::inference::CountNet exactly):

cp count_v1.safetensors v2/crates/cog-person-count/cog/artifacts/
cargo run -p cog-person-count --release -- health
# → {"backend":"candle-cpu", "synthetic_count": <int>, "synthetic_confidence": <float>, ...}